DocumentCode
940962
Title
An Instruction Throughput Model of Superscalar Processors
Author
Taha, Tarek M. ; Wills, D. Scott
Author_Institution
Clemson Univ., Clemson
Volume
57
Issue
3
fYear
2008
fDate
3/1/2008 12:00:00 AM
Firstpage
389
Lastpage
403
Abstract
Advances in semiconductor technology enable larger processor design spaces, leading to increasingly complex systems. At an initial stage, designers must evaluate many architecture design points to achieve a suitable design. Currently, most architecture exploration is performed using cycle accurate simulators. Although accurate, these tools are slow, thus limiting a comprehensive design search. The vast design space of today´s complex processors and time to market economic pressures motivate the need for faster architectural evaluation methods. This paper presents a superscalar processor performance model that enables rapid exploration of the architecture design space for superscalar processors. It supplements current design tools by quickly identifying promising areas for more thorough and time consuming exploration with traditional tools. The model estimates the instruction throughput of a superscalar processor based on early architectural design parameters and application properties. It has been validated with the SimpleScalar out-of-order simulator. The core of the model, which executes 1.6 million times faster, produces instruction throughput estimates that are with within 5.5 percent of the corresponding SimpleScalar values.
Keywords
microprocessor chips; SimpleScalar out-of-order simulator; architectural evaluation methods; cycle accurate simulators; instruction throughput model; semiconductor technology; superscalar processors; Analytical models; Application software; Computational modeling; Computer architecture; Delay; Out of order; Predictive models; Process design; Space technology; Throughput; Modeling of computer architecture; Modeling techniques; Pipeline processors;
fLanguage
English
Journal_Title
Computers, IEEE Transactions on
Publisher
ieee
ISSN
0018-9340
Type
jour
DOI
10.1109/TC.2007.70817
Filename
4358262
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